Camera-based object recognition systems are becoming increasingly important in automotive engineering, in particular for operating automated vehicles. Computer-assisted image processing systems are able to reliably identify different objects of interest such as road signs, lane markings, pedestrians or the like in taken camera images. So-called teaching processes or training processes are frequently used to develop and/or to adapt the corresponding recognition algorithms. A series of training images are presented to the image processing system in this process. They are specimen images which show typical objects to be recognized in the corresponding object surroundings, i.e. in an expected or natural environment.
Different teaching processes are known in the technical field of image-based object recognition; however, they all require a considerable number of different training images to achieve an acceptable teaching result. In many practical cases, work is carried out with so-called classifiers by means of which the objects recognized in an image are classified into different discrete object classes. There is a known problem with systems that require a relatively large number of classes because the gathering or obtaining of the necessary training images is time-consuming and arduous. This problem can be made worse when the objects of specific classes occur relatively rarely in reality, i.e. in a natural environment. There are, for example, fourteen different versions of road signs used in Germany to indicate speed limits—from 5 km/h (kph) up to 130 km/h. It is understood that in it is extremely complex in this case to generate training image data sets of sufficient size for all fourteen classes, in particular for the classes having road signs which occur more rarely such as the speed limits of 5 km/h or 110 km/h.